The present invention relates generally to machine-human interface and in particular the present invention relates to motion detection.
An increasing interest in the recognition of human motion and action using computer vision has appeared, with much emphasis on real-time computability. In particular, tracking/surveillance systems, human computer interfaces, and entertainment domains have a heightened interest in understanding and recognizing human movements. For example, monitoring applications may provide a signal only when a person is seen moving in a particular area (perhaps within a dangerous or secure area). Interface systems may be desired which understand gestures as a means of input or control, and entertainment applications may want to analyze the actions of the person to better aid in the immersion or reactivity of the experience.
In prior work, a real-time computer vision representation of human movement known as a Motion History Image (MHI) was presented. The MHI is a compact template representation of movement originally based on the layering of successive image motions. The recognition method presented for these motion templates used a global statistical moment feature vector constructed from image intensities, resulting in a token-based (label-based) matching scheme. Though this recognition method showed promising results using a large database of human movements, no method has yet been proposed to compute the raw motion information directly from the template without the necessity of labeling the entire motion pattern. Raw motion information may be favored for situations when a precisely labeled action is not possible or required. For example, a system may be designed to respond to leftward motion, but may not care if it was a person, hand, or car moving.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for a computer system for the representation and recognition of motion gestures.
In one embodiment, a method of detecting motion is provided which comprises obtaining a plurality of images of an object over a predetermined period of time, generating a motion region image of the object from the plurality of images, calculating normal gradients of the motion region image, removing erroneous normal gradients to provide a normal flow image, and identifying movement based on the normal flow image.
In another embodiment, a method of detecting motion is provided which comprises obtaining a plurality of images of an object using a camera. The plurality of images are obtained over a predetermined period of time. The method further comprises generating a motion region image of the object from the plurality of images by isolating the object in the plurality of images from a background, generating a difference image using the plurality of images, wherein the difference image isolates a portion of the object which has changed location during the plurality of images, and setting all pixels of the difference image to an equal value. Normal gradients of the motion region image are calculated, erroneous normal gradients are removed to provide a normal flow image, and movement is identified based on the normal flow image.